Design Thinking for Visual Analytics

Introduction

Approaching the end of the second decade of the 21st century, mobile management of one’s own health is becoming increasingly prevalent in western societies. This has been fostered by the declining costs of biomolecular testing [1] and Big Data advances in analysis methods for the omics and serves as foundations for a paradigm shift towards personalized and preventive medicine, increasing the need for health care consumers to directly access and interact with the digital version of their own records

The field of Visual Analytics (VA), defined as “the science of analytical reasoning facilitated by interactive visual interfaces” [2] grew out of Information Visualization (InfoVis) and Scientific Visualization (SciVis) to become a highly interdisciplinary discipline that is mainly informed by computer science and cognitive science, placing a focus on the human as the center of research.

Methods from VA can support integrative analysis that consider both, the health data analysts and health consumers’ needs in an information communication loop. By doing so, it’s possible to focus on data infrastructures that contain controlled vocabularies to facilitate the communication between both parties. VA con provide frameworks to account for the health consumer’s experience in the design of analytical-support systems for analysts.

Problem

Symptoms can be related to body part/system/activity

Access to digital health is mediated by digital interfaces

Data Analysts and Health Consumers rarely talk

Design Thinking

Design Thinking (DT) is a term that has shown a growth in its use, with applications being found in disciplines ranging from business to IT to healthcare. One of the main reasons for the rise in popularity is the seemingly universal applicability to any type of problem.

Stanford’s 5-Step Design Thinking Framework

In popular culture, Stanfords 5-step DT [3] approach has seen a spread of the methodology and its implementations across different fields, yet this has also caused an oversimplification of the methodology and a lack of focus on the research component. In that sense, Nielsen Norman’s framework [4] is rooted in research which can scaffold methodologies and methods that can be applied to the design and evaluation of VA systems.

Norman Nielsen Design Thinking Framework

Research Questions

How to design an interactive system for capturing descriptive patient data to support automatization in precision medicine?

Overarching Mixed Methods Research Question

How can a distributed cognition lens for short-term ethnography inform the design of visual analytics systems in the context of correlating patient descriptive data with biomolecular data?

Qualitative Research Question

Can an interactive system for capturing descriptive patient data help patients to accurately report their conditions?

Quantitative Research Question

Methodology & Methods

Adapted Design Thinking Framework to incorporate Mixed Methods for Visual Analytics

Prototype Design

  • Overview of the system
  • I) Mapping the Human Phenotype Ontology to the categories for the Prototype buttons. Categories were refined with input from lay users.
  • II) Mapping the HPO to the buttons and the body graphic. Example of Visual feedback for the “Cardio” category
  • III) Prototype: Introduction Scene where the user reports symptoms + gets introduced to the somatic representation
  • IV) Prototype: Interactive Scene where the users selects conditions. The visual feedback adds up when the user selects conditions from multiple body systems/categories.